The need for improved semiconductor inspection and review tools continues to grow. For example, there is a need for improved quantification of defects detected by optical and SEM based inspection methods. Due to semiconductor device design rules (14 nm (production), 10 nm (pilot) and 7 nm (R&D)) and the complexity associated with multi-patterning the ability to distinguish a defect from potential noise is a significant challenge. Distinguishing a defect from noise is even more challenging in the case of systematic defects that may be related to subtle variations of critical patterns. Currently, optical and SEM inspection methods, such as process window discovery, are used to identify such systematic defects and sample these ‘hot spots’ for review. This approach relies on the use of modulated fields within a wafer to cause the weakest structures to fail in order to enhance detection, allowing for the identification of the edge of the process window.
Hot spots detected by optical inspection must be verified by a defect review tool, such as a scanning electron microscopy (SEM) review tool, to localize the exact point of failure and understand whether the failure will have a significant impact on the device. Typically, SEM review tools have been used for classification of defects by binning the defects into different bins based on defect type. In the case of hot spot classification, manual classification is typically employed to group defects into good, bad and marginal classes, which may be subjective. Recent advances have provided for the incorporation of design information in design-assisted automatic classification. Nevertheless, even in design-assisted classification, a vast amount of information is lost due to the inability to specifically quantify features in SEM review images. Feature quantification is particularly important in understanding pattern fidelities at the current advanced design rule nodes and future nodes. Therefore, it would be desirable to provide a system and method for curing the shortcomings of prior approaches such as those identified above.